What's in a symbol: ontology, representation and language

Ниренбург, Сергей; Wilks, Yorick

doi:10.1080/09528130010029811

Cited by 22 publications

(17 citation statements)

References 3 publications

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“…Although the theory of Ontological Semantics, as described in Nirenburg and Raskin (2004), has provided sufficient conceptual underpinnings for the linguistic and algorithmic enhancements that have followed since, it was the practical, post-theoretical experience of treating difficult lexical issues that led us to view the lexicon as largely language independent. Stated more precisely, the most difficult aspect of lexical acquisition-describing meanings of words and phrases using a complex storehouse of expressive means-need be done only once.…”

Section: The Theory Of Ontological Semantics and The Ontosem Environmentmentioning

confidence: 99%

“…The former offer some practical solutions for the dominant language pairs but do not offer broad language coverage or robust disambiguation. The latter tend to ambiguate more than they disambiguate, making them a poor option for knowledge-rich NLP (see Nirenburg et al 2004 for a critique of WordNet as a resource for NLP). The SIMPLE lexicons (Lenci et al 2000a, b) are an interesting hybrid: they use EuroWordNet as an anchor to guide development of otherwise separate lexicons for different languages.…”

Section: Ontologically Grounded Vs Non-ontologically Grounded Lexiconsmentioning

confidence: 99%

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An NLP Lexicon as a Largely Language-Independent Resource

McShane

Ниренбург

Beale

2005

Machine Translation

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This paper describes salient aspects of the OntoSem lexicon of English, a lexicon whose semantic descriptions can either be grounded in a language-independent ontology, rely on extra-ontological expressive means, or exploit a combination of the two. The variety of descriptive means, as well as the conceptual complexity of semantic description to begin with, necessitates that OntoSem lexicons be compiled primarily manually. However, once a semantic description is created for a lexeme in one language, it can be reused in others, often with little or no modification. Said differently, the challenge in building a semantic lexicon is describing semantics; once the semantics are described, it is relatively straightforward to connect given meanings to the appropriate head words in other languages. In this paper we provide a brief overview of the OntoSem lexicon and processing environment, orient our approach to lexical semantics among others in the field, and describe in more detail what we mean by the largely language-independent lexicon. Finally, we suggest reasons why our resources might be of interest to the larger community.

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Section: The Theory Of Ontological Semantics and The Ontosem Environmentmentioning

confidence: 99%

Section: Ontologically Grounded Vs Non-ontologically Grounded Lexiconsmentioning

confidence: 99%

An NLP Lexicon as a Largely Language-Independent Resource

McShane

Ниренбург

Beale

2005

Machine Translation

Self Cite

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“…This capability is extremely useful when one separates word senses from ontology concepts. As argued in for example [37] and discussed in [17], not all wordsenses in the sense hierarchy need (or should) be converted into ontological categories (concepts). The sense hierarchicalization for "drive" above, for example, requires at least three distinct concepts, namely "drive mad" (i.e., something like Cause-Mental-Instability), the nonphysical sense group (rooted in something like Cause-State-Changetoward-Desired-Value), and the physical group (rooted in Cause-Movement-in-Desired-Direction) respectively.…”

Section: From Words To Concepts: Hierarchical Graduated Refinementmentioning

confidence: 99%

Methodologies for the Reliable Construction of Ontological Knowledge

Hovy

2005

Conceptual Structures: Common Semantics for Sharing Knowledge

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“…A large part of the community seems to recognise that the content expressed in formal representation languages, such as the semantic web ones, should be accessible not only to logical reasoning machines but also to humans and NLP procedures, and thus resemble the natural language as much as possible [17].…”

Section: Ontology Repair With Patomatmentioning

confidence: 99%

Knowledge Base Creation, Enrichment and Repair

Hellmann

Bryl

Bühmann

et al. 2014

Linked Open Data -- Creating Knowledge Out of Interlinked Data

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Abstract. This chapter focuses on data transformation to RDF and Linked Data and furthermore on the improvement of existing or extracted data especially with respect to schema enrichment and ontology repair. Tasks concerning the triplification of data are mainly grounded on existing and well-proven techniques and were refined during the lifetime of the LOD2 project and integrated into the LOD2 Stack. Triplification of legacy data, i.e. data not yet in RDF, represents the entry point for legacy systems to participate in the LOD cloud. While existing systems are often very useful and successful, there are notable differences between the ways knowledge bases and Wikis or databases are created and used. One of the key differences in content is in the importance and use of schematic information in knowledge bases. This information is usually absent in the source system and therefore also in many LOD knowledge bases. However, schema information is needed for consistency checking and finding modelling problems. We will present a combination of enrichment and repair steps to tackle this problem based on previous research in machine learning and knowledge representation. Overall, the Chapter describes how to enable tool-supported creation and publishing of RDF as Linked Data (Sect. 1) and how to increase the quality and value of such large knowledge bases when published on the Web (Sect. 2).

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What's in a symbol: ontology, representation and language

Cited by 22 publications

References 3 publications

An NLP Lexicon as a Largely Language-Independent Resource

An NLP Lexicon as a Largely Language-Independent Resource

Methodologies for the Reliable Construction of Ontological Knowledge

Knowledge Base Creation, Enrichment and Repair

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